Coverage for python/lsst/ap/association/transformDiaSourceCatalog.py: 20%
167 statements
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« prev ^ index » next coverage.py v7.2.7, created at 2023-06-02 03:03 -0700
1# This file is part of ap_association
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ("TransformDiaSourceCatalogConnections",
23 "TransformDiaSourceCatalogConfig",
24 "TransformDiaSourceCatalogTask",
25 "UnpackApdbFlags")
27import numpy as np
28import os
29import yaml
30import pandas as pd
32from lsst.daf.base import DateTime
33import lsst.pex.config as pexConfig
34import lsst.pipe.base as pipeBase
35import lsst.pipe.base.connectionTypes as connTypes
36from lsst.meas.base import DetectorVisitIdGeneratorConfig
37from lsst.pipe.tasks.postprocess import TransformCatalogBaseTask, TransformCatalogBaseConfig
38from lsst.pipe.tasks.functors import Column
39from lsst.utils.timer import timeMethod
42class TransformDiaSourceCatalogConnections(pipeBase.PipelineTaskConnections,
43 dimensions=("instrument", "visit", "detector"),
44 defaultTemplates={"coaddName": "deep", "fakesType": ""}):
45 diaSourceSchema = connTypes.InitInput(
46 doc="Schema for DIASource catalog output by ImageDifference.",
47 storageClass="SourceCatalog",
48 name="{fakesType}{coaddName}Diff_diaSrc_schema",
49 )
50 diaSourceCat = connTypes.Input(
51 doc="Catalog of DiaSources produced during image differencing.",
52 name="{fakesType}{coaddName}Diff_diaSrc",
53 storageClass="SourceCatalog",
54 dimensions=("instrument", "visit", "detector"),
55 )
56 diffIm = connTypes.Input(
57 doc="Difference image on which the DiaSources were detected.",
58 name="{fakesType}{coaddName}Diff_differenceExp",
59 storageClass="ExposureF",
60 dimensions=("instrument", "visit", "detector"),
61 )
62 spuriousness = connTypes.Input(
63 doc="Spuriousness (e.g. real/bogus) classificiation of diaSourceCat sources (optional).",
64 name="{fakesType}{coaddName}RealBogusSources",
65 storageClass="Catalog",
66 dimensions=("instrument", "visit", "detector"),
67 )
68 diaSourceTable = connTypes.Output(
69 doc=".",
70 name="{fakesType}{coaddName}Diff_diaSrcTable",
71 storageClass="DataFrame",
72 dimensions=("instrument", "visit", "detector"),
73 )
75 def __init__(self, *, config=None):
76 super().__init__(config=config)
77 if not self.config.doIncludeSpuriousness:
78 self.inputs.remove("spuriousness")
81class TransformDiaSourceCatalogConfig(TransformCatalogBaseConfig,
82 pipelineConnections=TransformDiaSourceCatalogConnections):
83 flagMap = pexConfig.Field(
84 dtype=str,
85 doc="Yaml file specifying SciencePipelines flag fields to bit packs.",
86 default=os.path.join("${AP_ASSOCIATION_DIR}",
87 "data",
88 "association-flag-map.yaml"),
89 )
90 flagRenameMap = pexConfig.Field(
91 dtype=str,
92 doc="Yaml file specifying specifying rules to rename flag names",
93 default=os.path.join("${AP_ASSOCIATION_DIR}",
94 "data",
95 "flag-rename-rules.yaml"),
96 )
97 doRemoveSkySources = pexConfig.Field(
98 dtype=bool,
99 default=False,
100 doc="Input DiaSource catalog contains SkySources that should be "
101 "removed before storing the output DiaSource catalog."
102 )
103 doPackFlags = pexConfig.Field(
104 dtype=bool,
105 default=True,
106 doc="Do pack the flags into one integer column named 'flags'."
107 "If False, instead produce one boolean column per flag."
108 )
109 doIncludeSpuriousness = pexConfig.Field(
110 dtype=bool,
111 default=False,
112 doc="Include the spuriousness (e.g. real/bogus) classifications in the output."
113 )
115 idGenerator = DetectorVisitIdGeneratorConfig.make_field()
117 def setDefaults(self):
118 super().setDefaults()
119 self.functorFile = os.path.join("${AP_ASSOCIATION_DIR}",
120 "data",
121 "DiaSource.yaml")
124class TransformDiaSourceCatalogTask(TransformCatalogBaseTask):
125 """Transform a DiaSource catalog by calibrating and renaming columns to
126 produce a table ready to insert into the Apdb.
128 Parameters
129 ----------
130 initInputs : `dict`
131 Must contain ``diaSourceSchema`` as the schema for the input catalog.
132 """
133 ConfigClass = TransformDiaSourceCatalogConfig
134 _DefaultName = "transformDiaSourceCatalog"
135 # Needed to create a valid TransformCatalogBaseTask, but unused
136 inputDataset = "deepDiff_diaSrc"
137 outputDataset = "deepDiff_diaSrcTable"
139 def __init__(self, initInputs, **kwargs):
140 super().__init__(**kwargs)
141 self.funcs = self.getFunctors()
142 self.inputSchema = initInputs['diaSourceSchema'].schema
143 self._create_bit_pack_mappings()
145 if not self.config.doPackFlags:
146 # get the flag rename rules
147 with open(os.path.expandvars(self.config.flagRenameMap)) as yaml_stream:
148 self.rename_rules = list(yaml.safe_load_all(yaml_stream))
150 def _create_bit_pack_mappings(self):
151 """Setup all flag bit packings.
152 """
153 self.bit_pack_columns = []
154 flag_map_file = os.path.expandvars(self.config.flagMap)
155 with open(flag_map_file) as yaml_stream:
156 table_list = list(yaml.safe_load_all(yaml_stream))
157 for table in table_list:
158 if table['tableName'] == 'DiaSource':
159 self.bit_pack_columns = table['columns']
160 break
162 # Test that all flags requested are present in the input schemas.
163 # Output schemas are flexible, however if names are not specified in
164 # the Apdb schema, flag columns will not be persisted.
165 for outputFlag in self.bit_pack_columns:
166 bitList = outputFlag['bitList']
167 for bit in bitList:
168 try:
169 self.inputSchema.find(bit['name'])
170 except KeyError:
171 raise KeyError(
172 "Requested column %s not found in input DiaSource "
173 "schema. Please check that the requested input "
174 "column exists." % bit['name'])
176 def runQuantum(self, butlerQC, inputRefs, outputRefs):
177 inputs = butlerQC.get(inputRefs)
178 idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
179 inputs["ccdVisitId"] = idGenerator.catalog_id
180 inputs["band"] = butlerQC.quantum.dataId["band"]
182 outputs = self.run(**inputs)
184 butlerQC.put(outputs, outputRefs)
186 @timeMethod
187 def run(self,
188 diaSourceCat,
189 diffIm,
190 band,
191 ccdVisitId,
192 spuriousness=None):
193 """Convert input catalog to ParquetTable/Pandas and run functors.
195 Additionally, add new columns for stripping information from the
196 exposure and into the DiaSource catalog.
198 Parameters
199 ----------
200 diaSourceCat : `lsst.afw.table.SourceCatalog`
201 Catalog of sources measured on the difference image.
202 diffIm : `lsst.afw.image.Exposure`
203 Result of subtracting template and science images.
204 band : `str`
205 Filter band of the science image.
206 ccdVisitId : `int`
207 Identifier for this detector+visit.
208 spuriousness : `lsst.afw.table.SourceCatalog`
209 Spuriousness (e.g. real/bogus) scores, row-matched to
210 ``diaSourceCat``.
212 Returns
213 -------
214 results : `lsst.pipe.base.Struct`
215 Results struct with components.
217 - ``diaSourceTable`` : Catalog of DiaSources with calibrated values
218 and renamed columns.
219 (`lsst.pipe.tasks.ParquetTable` or `pandas.DataFrame`)
220 """
221 self.log.info(
222 "Transforming/standardizing the DiaSource table ccdVisitId: %i",
223 ccdVisitId)
225 diaSourceDf = diaSourceCat.asAstropy().to_pandas()
226 if self.config.doRemoveSkySources:
227 diaSourceDf = diaSourceDf[~diaSourceDf["sky_source"]]
228 diaSourceCat = diaSourceCat[~diaSourceCat["sky_source"]]
230 diaSourceDf["snr"] = getSignificance(diaSourceCat)
231 diaSourceDf["bboxSize"] = self.computeBBoxSizes(diaSourceCat)
232 diaSourceDf["ccdVisitId"] = ccdVisitId
233 diaSourceDf["filterName"] = band
234 diaSourceDf["midPointTai"] = diffIm.getInfo().getVisitInfo().getDate().get(system=DateTime.MJD)
235 diaSourceDf["diaObjectId"] = 0
236 diaSourceDf["ssObjectId"] = 0
238 if self.config.doIncludeSpuriousness:
239 spuriousnessDf = spuriousness.asAstropy().to_pandas()
240 # This uses the pandas index to match scores with diaSources
241 # but it will silently fill with NaNs if they don't match.
242 diaSourceDf = pd.merge(diaSourceDf, spuriousnessDf,
243 how="left", on="id", validate="1:1")
244 diaSourceDf = diaSourceDf.rename(columns={"score": "spuriousness"})
245 if np.sum(diaSourceDf["spuriousness"].isna()) == len(diaSourceDf):
246 self.log.warning("Spuriousness identifiers did not match diaSourceIds")
247 else:
248 diaSourceDf["spuriousness"] = np.float32(np.nan)
250 if self.config.doPackFlags:
251 # either bitpack the flags
252 self.bitPackFlags(diaSourceDf)
253 else:
254 # or add the individual flag functors
255 self.addUnpackedFlagFunctors()
256 # and remove the packed flag functor
257 if 'flags' in self.funcs.funcDict:
258 del self.funcs.funcDict['flags']
260 df = self.transform(band,
261 diaSourceDf,
262 self.funcs,
263 dataId=None).df
265 return pipeBase.Struct(
266 diaSourceTable=df,
267 )
269 def addUnpackedFlagFunctors(self):
270 """Add Column functor for each of the flags to the internal functor
271 dictionary.
272 """
273 for flag in self.bit_pack_columns[0]['bitList']:
274 flagName = flag['name']
275 targetName = self.funcs.renameCol(flagName, self.rename_rules[0]['flag_rename_rules'])
276 self.funcs.update({targetName: Column(flagName)})
278 def computeBBoxSizes(self, inputCatalog):
279 """Compute the size of a square bbox that fully contains the detection
280 footprint.
282 Parameters
283 ----------
284 inputCatalog : `lsst.afw.table.SourceCatalog`
285 Catalog containing detected footprints.
287 Returns
288 -------
289 outputBBoxSizes : `np.ndarray`, (N,)
290 Array of bbox sizes.
291 """
292 # Schema validation requires that this field is int.
293 outputBBoxSizes = np.empty(len(inputCatalog), dtype=int)
294 for i, record in enumerate(inputCatalog):
295 footprintBBox = record.getFootprint().getBBox()
296 # Compute twice the size of the largest dimension of the footprint
297 # bounding box. This is the largest footprint we should need to cover
298 # the complete DiaSource assuming the centroid is within the bounding
299 # box.
300 maxSize = 2 * np.max([footprintBBox.getWidth(),
301 footprintBBox.getHeight()])
302 recX = record.getCentroid().x
303 recY = record.getCentroid().y
304 bboxSize = int(
305 np.ceil(2 * np.max(np.fabs([footprintBBox.maxX - recX,
306 footprintBBox.minX - recX,
307 footprintBBox.maxY - recY,
308 footprintBBox.minY - recY]))))
309 if bboxSize > maxSize:
310 bboxSize = maxSize
311 outputBBoxSizes[i] = bboxSize
313 return outputBBoxSizes
315 def bitPackFlags(self, df):
316 """Pack requested flag columns in inputRecord into single columns in
317 outputRecord.
319 Parameters
320 ----------
321 df : `pandas.DataFrame`
322 DataFrame to read bits from and pack them into.
323 """
324 for outputFlag in self.bit_pack_columns:
325 bitList = outputFlag['bitList']
326 value = np.zeros(len(df), dtype=np.uint64)
327 for bit in bitList:
328 # Hard type the bit arrays.
329 value += (df[bit['name']]*2**bit['bit']).to_numpy().astype(np.uint64)
330 df[outputFlag['columnName']] = value
333class UnpackApdbFlags:
334 """Class for unpacking bits from integer flag fields stored in the Apdb.
336 Attributes
337 ----------
338 flag_map_file : `str`
339 Absolute or relative path to a yaml file specifiying mappings of flags
340 to integer bits.
341 table_name : `str`
342 Name of the Apdb table the integer bit data are coming from.
343 """
345 def __init__(self, flag_map_file, table_name):
346 self.bit_pack_columns = []
347 flag_map_file = os.path.expandvars(flag_map_file)
348 with open(flag_map_file) as yaml_stream:
349 table_list = list(yaml.safe_load_all(yaml_stream))
350 for table in table_list:
351 if table['tableName'] == table_name:
352 self.bit_pack_columns = table['columns']
353 break
355 self.output_flag_columns = {}
357 for column in self.bit_pack_columns:
358 names = []
359 for bit in column["bitList"]:
360 names.append((bit["name"], bool))
361 self.output_flag_columns[column["columnName"]] = names
363 def unpack(self, input_flag_values, flag_name):
364 """Determine individual boolean flags from an input array of unsigned
365 ints.
367 Parameters
368 ----------
369 input_flag_values : array-like of type uint
370 Array of integer flags to unpack.
371 flag_name : `str`
372 Apdb column name of integer flags to unpack. Names of packed int
373 flags are given by the flag_map_file.
375 Returns
376 -------
377 output_flags : `numpy.ndarray`
378 Numpy named tuple of booleans.
379 """
380 bit_names_types = self.output_flag_columns[flag_name]
381 output_flags = np.zeros(len(input_flag_values), dtype=bit_names_types)
383 for bit_idx, (bit_name, dtypes) in enumerate(bit_names_types):
384 masked_bits = np.bitwise_and(input_flag_values, 2**bit_idx)
385 output_flags[bit_name] = masked_bits
387 return output_flags
389 def flagExists(self, flagName, columnName='flags'):
390 """Check if named flag is in the bitpacked flag set.
392 Parameters:
393 ----------
394 flagName : `str`
395 Flag name to search for.
396 columnName : `str`, optional
397 Name of bitpacked flag column to search in.
399 Returns
400 -------
401 flagExists : `bool`
402 `True` if `flagName` is present in `columnName`.
404 Raises
405 ------
406 ValueError
407 Raised if `columnName` is not defined.
408 """
409 if columnName not in self.output_flag_columns:
410 raise ValueError(f'column {columnName} not in flag map: {self.output_flag_columns}')
412 return flagName in [c[0] for c in self.output_flag_columns[columnName]]
414 def makeFlagBitMask(self, flagNames, columnName='flags'):
415 """Return a bitmask corresponding to the supplied flag names.
417 Parameters:
418 ----------
419 flagNames : `list` [`str`]
420 Flag names to include in the bitmask.
421 columnName : `str`, optional
422 Name of bitpacked flag column.
424 Returns
425 -------
426 bitmask : `np.unit64`
427 Bitmask corresponding to the supplied flag names given the loaded configuration.
429 Raises
430 ------
431 ValueError
432 Raised if a flag in `flagName` is not included in `columnName`.
433 """
434 bitmask = np.uint64(0)
436 for flag in flagNames:
437 if not self.flagExists(flag, columnName=columnName):
438 raise ValueError(f"flag '{flag}' not included in '{columnName}' flag column")
440 for outputFlag in self.bit_pack_columns:
441 if outputFlag['columnName'] == columnName:
442 bitList = outputFlag['bitList']
443 for bit in bitList:
444 if bit['name'] in flagNames:
445 bitmask += np.uint64(2**bit['bit'])
447 return bitmask
450def getSignificance(catalog):
451 """Return the significance value of the first peak in each source
452 footprint, or NaN for peaks without a significance field.
454 Parameters
455 ----------
456 catalog : `lsst.afw.table.SourceCatalog`
457 Catalog to process.
459 Returns
460 -------
461 significance : `np.ndarray`, (N,)
462 Signficance of the first peak in each source footprint.
463 """
464 result = np.full(len(catalog), np.nan)
465 for i, record in enumerate(catalog):
466 peaks = record.getFootprint().peaks
467 if "significance" in peaks.schema:
468 result[i] = peaks[0]["significance"]
469 return result